replicate-flux-mcp

Server Quality Checklist

Profile completionA complete profile improves this server's visibility in search results.

Latest release: v1.0.0
Disambiguation3/5
The tools have some clear distinctions, such as between image generation and prediction management, but there is significant overlap and ambiguity among the image generation tools. For example, 'generate_image' and 'create_prediction' both seem to create images from prompts, and 'generate_image_variants' and 'generate_multiple_images' could be confused for handling multiple outputs in similar ways.
Naming Consistency4/5
The naming is mostly consistent with a verb_noun pattern, such as 'create_prediction' and 'generate_image', but there are minor deviations like 'prediction_list' (noun_verb) and inconsistent use of 'generate' vs. 'create'. Overall, the pattern is readable and predictable with only slight variations.
Tool Count5/5
With 7 tools, the count is well-scoped for an image generation server, covering core operations like creating, retrieving, and listing predictions, as well as various image generation methods. Each tool appears to serve a distinct purpose within this domain, making the set appropriately sized.
Completeness4/5
The tool set provides good coverage for image generation and prediction management, including creation, retrieval, and listing. However, there is a minor gap in update or delete operations for predictions or images, which might limit full lifecycle management but is not critical for basic usage.
Average 2.9/5 across 7 of 7 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- 1 of 2 community issues answered or closed in the last 6 months
- 0 commits in the last 12 weeks
- Last stable release on April 19, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
Add a glama.json file to provide metadata about your server.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the model ('Flux Schnell') but doesn't describe what 'prediction' means in this context (likely image generation), expected outputs, rate limits, authentication needs, or potential costs. For a tool with 10 parameters and no annotations, this is a significant gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence: 'Generate an prediction from a text prompt using Flux Schnell model.' It's front-loaded with the core purpose and has zero wasted words. The minor grammatical error ('an prediction') doesn't impact clarity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (10 parameters, no output schema, no annotations), the description is insufficient. It doesn't explain what a 'prediction' is in this context (likely an image), the output format (though hinted by parameters like 'output_format'), or behavioral aspects like generation time or error handling. For an image generation tool with many parameters, more context is needed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, so all parameters are documented in the schema. The description doesn't add any parameter-specific information beyond what's in the schema. It mentions 'text prompt' which aligns with the 'prompt' parameter, but this is already covered. Baseline 3 is appropriate when the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Generate an prediction from a text prompt using Flux Schnell model.' It specifies the verb ('Generate'), resource ('prediction'), and model ('Flux Schnell'), but doesn't explicitly differentiate from sibling tools like 'generate_image' or 'get_prediction'. The description is clear but lacks sibling differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like 'generate_image' or 'generate_image_variants'. It doesn't mention prerequisites, constraints, or comparative use cases. The agent must infer usage from the tool name and parameters alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It mentions the model ('Flux Schnell') but doesn't describe rate limits, authentication needs, cost implications, or what happens when generation fails. For a complex image generation tool with 11 parameters, this leaves significant behavioral gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that states the core functionality without unnecessary elaboration. Every word earns its place, and the information is front-loaded with the essential action and method.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a complex image generation tool with 11 parameters and no annotations or output schema, the description is inadequate. It doesn't explain what the output looks like (e.g., image URLs, binary data), error conditions, or practical limitations. The agent lacks sufficient context to use this tool effectively beyond basic invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, so the schema already documents all 11 parameters thoroughly. The description doesn't add any parameter-specific context beyond what's in the schema, such as guidance on prompt engineering or trade-offs between parameters. Baseline 3 is appropriate when schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('generate') and resource ('image') with the specific method ('from a text prompt using Flux Schnell model'). It distinguishes from siblings like 'generate_svg' by specifying image generation, but doesn't explicitly differentiate from 'generate_image_variants' or 'generate_multiple_images' which are closely related.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like 'generate_image_variants' or 'generate_multiple_images'. There's no mention of prerequisites, constraints, or comparative use cases with sibling tools, leaving the agent to infer usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the core action (generating image variants) but lacks critical behavioral details: it doesn't mention whether this is a read-only or mutating operation, what permissions might be required, rate limits, cost implications, or what the output looks like (e.g., file format, size, or how variants are returned). For a complex tool with 13 parameters, this is a significant gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose without any fluff or redundancy. It's appropriately sized and front-loaded, with every word earning its place. No structural issues or unnecessary elaboration are present.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (13 parameters, no annotations, no output schema), the description is incomplete. It adequately states what the tool does but fails to address behavioral aspects (e.g., whether it's a costly operation, what the output contains), usage guidelines relative to siblings, or any higher-level context. For a generative AI tool with many configuration options, more guidance would be helpful.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, meaning all parameters are well-documented in the schema itself. The description adds no additional parameter information beyond what's in the schema (e.g., it doesn't explain parameter interactions or provide usage examples). According to the rules, when schema coverage is high (>80%), the baseline score is 3 even with no param info in the description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Generate multiple variants of the same image from a single prompt.' It specifies the verb ('generate'), resource ('image variants'), and scope ('multiple variants from a single prompt'). However, it doesn't explicitly distinguish this from sibling tools like 'generate_image' or 'generate_multiple_images', which likely have different functionality.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. There are several sibling tools (e.g., 'generate_image', 'generate_multiple_images') that appear related, but the description doesn't explain how this tool differs from them or when it's the appropriate choice. No usage context, prerequisites, or exclusions are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden but offers minimal behavioral insight. It mentions the model ('Flux Schnell') but doesn't disclose performance characteristics (e.g., speed, cost), error handling, rate limits, or what happens when prompts fail. For a generative AI tool with 10 parameters, this leaves significant gaps in understanding its operational behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core functionality. Every word earns its place, with no redundant or vague phrasing. It's appropriately sized for the tool's complexity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a complex image generation tool with 10 parameters, no annotations, and no output schema, the description is inadequate. It doesn't explain what the tool returns (e.g., image URLs, metadata), error conditions, or practical limitations. The absence of output schema means the description should compensate by describing outputs, but it doesn't.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so all parameters are documented in the schema. The description adds no additional parameter semantics beyond what's in the schema—it doesn't explain how 'prompts' array relates to output, or provide context for parameter interactions. Baseline 3 is appropriate when schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Generate multiple images') and the resource ('from an array of prompts'), specifying the model used ('Flux Schnell model'). It distinguishes from sibling 'generate_image' by indicating multiple images from multiple prompts, but doesn't explicitly contrast with other siblings like 'generate_image_variants' or 'generate_svg'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like 'generate_image' (single image) or 'generate_image_variants' (variations of one image). There's no mention of prerequisites, constraints, or typical use cases beyond the basic functionality.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It states what the tool does but doesn't mention rate limits, authentication requirements, whether it's a read-only or mutation operation, what happens on failure, or what the output format looks like. For a generative tool with zero annotation coverage, this leaves significant behavioral gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that communicates the core purpose without any wasted words. It's appropriately sized for a straightforward generation tool and front-loads the essential information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a generative tool with no annotations and no output schema, the description is insufficient. It doesn't explain what the tool returns (SVG data, URL, file path?), error conditions, rate limits, or authentication requirements. The schema handles parameters well, but the overall context for proper tool invocation is incomplete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all three parameters thoroughly with descriptions, enums, and defaults. The description adds no additional parameter information beyond what's in the schema. According to scoring rules, when schema coverage is high (>80%), the baseline is 3 even with no param info in description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'generate' and resource 'SVG', specifying it uses the Recraft model from a text prompt. It distinguishes from sibling tools like 'generate_image' by specifying SVG format rather than general images. However, it doesn't explicitly differentiate from other SVG-related tools (none exist among siblings), so it's not a perfect 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like 'generate_image' or 'generate_multiple_images'. It doesn't mention use cases, prerequisites, or exclusions. The only contextual clue is 'using Recraft model', which doesn't help with sibling tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool retrieves details but doesn't describe what 'details' include (e.g., status, output, metadata), whether it's a read-only operation, error handling, or performance characteristics. This leaves significant gaps for an agent to understand the tool's behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with zero waste. It's front-loaded with the core purpose and uses clear language. Every word earns its place, making it easy for an agent to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (retrieving a specific resource) and lack of annotations and output schema, the description is incomplete. It doesn't explain what 'details' are returned, error conditions, or how this fits into broader workflows with siblings. For a tool with no structured output information, more context is needed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, with the parameter 'predictionId' clearly documented in the schema. The description adds minimal value beyond the schema by implying the parameter is required ('by ID') but doesn't provide additional context like format examples or constraints. Baseline 3 is appropriate since the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with a specific verb ('Get') and resource ('prediction'), and identifies the key input ('by ID'). It distinguishes from siblings like 'prediction_list' (which retrieves multiple predictions) by focusing on a single prediction. However, it doesn't specify what 'details' include, leaving some ambiguity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention when to choose 'get_prediction' over 'prediction_list' (for listing multiple predictions) or other siblings like 'create_prediction'. There's no context about prerequisites, such as needing an existing prediction ID.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions 'recent predictions' but doesn't specify timeframes, ordering, pagination, authentication requirements, rate limits, or what constitutes 'recent'. This leaves significant gaps in understanding the tool's behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose without unnecessary words. It's appropriately sized and front-loaded, making it easy to understand immediately.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with no annotations and no output schema, the description is insufficient. It doesn't explain what the returned predictions contain, their format, or how 'recent' is defined. Given the lack of structured data, more descriptive context is needed for the agent to use this tool effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, with the single parameter 'limit' well-documented in the schema. The description doesn't add any parameter-specific information beyond what the schema already provides, so it meets the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Get a list') and resource ('recent predictions from Replicate'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'get_prediction' which might retrieve a single prediction, leaving room for ambiguity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives like 'get_prediction' or 'create_prediction'. The description only states what it does, not when it's appropriate or what distinguishes it from similar tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

GitHub Badge

Glama performs regular codebase and documentation scans to:

Confirm that the MCP server is working as expected.
Confirm that there are no obvious security issues.
Evaluate tool definition quality.

Our badge communicates server capabilities, safety, and installation instructions.

Card Badge

Copy to your README.md:

[![replicate-flux-mcp MCP server](https://glama.ai/mcp/servers/awkoy/replicate-flux-mcp/badges/card.svg)](https://glama.ai/mcp/servers/awkoy/replicate-flux-mcp)

Score Badge

Copy to your README.md:

[![replicate-flux-mcp MCP server](https://glama.ai/mcp/servers/awkoy/replicate-flux-mcp/badges/score.svg)](https://glama.ai/mcp/servers/awkoy/replicate-flux-mcp)

Latest Blog Posts

Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)
By Om-Shree-0709 on July 25, 2026.
mcp
Agent Identity
OAuth 2.1
Your AI Chatbot Just Exposed Your CEO's Salary to an Intern
By Om-Shree-0709 on July 2, 2026.
Agent Identity
MCP Security
OAuth Delegation
Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)
By Om-Shree-0709 on June 30, 2026.
Agentic Ai
Prompt Injection
WebAssembly

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/awkoy/replicate-flux-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server